A simple selection strategy is to randomly select \(BD\)  samples for each of the BF and the PA classes to store in the memory buffer.
Multiple strategies have been used in the CL literature to improve upon this baseline sampling strategy, including, mean of features (MoF) [1]}, ring buffer [2]}, and reservoir sampling [3]}. Since we learn the prototypical distribution as a GMM, we can also rely on a strategy similar to MoF. After training the model in the binary classification setting and fitting the GMM, we can compute the distance of all BFs and PAs from their corresponding Gaussian component's mean as \(d^t_{j,k}=\Vert \mu _j^t-\mathbf {x}^t_k\Vert _2^2 \mbox{ } \forall \mathbf {x}^t_k \mbox{ s.t. } \hat{\mathbf {y}}_k^t=j\) .
We sort these distances, for each class separately, and given the per-class memory budget \(BD\) , we store the samples that are closest to the cluster means. Note that as opposed to a normal CL setting, the labels are predicted for novel PAs in our setting, for \(t\ge 1\) . Hence, it is more likely that labels for samples close to the means are predicted correctly. However, information about higher moments of the distribution is lost when these samples are used for pseudo-rehearsal. As a result, the model prediction accuracy may reduce in the area close to the boundary of the classes in the future.
